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Computationally Efficient Constraint-Optimized Radar Mismatched Filtering for Waveform-Agile systems

IEEE Transactions on Aerospace and Electronic systems 2025年第3期61卷 6711-6732页

作者： Jones, Christian C. McCormick, Patrick M. Heintzelman, Matthew B. U.S. Naval Research Laboratory WashingtonDC20375 United States University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States University of Kansas Institute for Information Sciences LawrenceKS66045 United States

Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression;however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficiently computed (for possible real-time operation) with consideration of possible interference via collisions with other spectral users. Here, a computationally efficient radar mismatched filter (MMF) design framework is proposed which provides the optimal integrated sidelobe level (ISL) or spectral template match for a constrained signal-to-interference-plus-noise (SINR) loss via the Lagrange dual problem with known interference. This approach builds off of previous works which examine ISL MMFs with constrained signal-to-noise (SNR) loss and provides further extensions, analysis, and bounds for alternative quadratic constraints and design objectives. Furthermore, by leveraging the Toeplitz structure that arises in time-series problems and the bounds of the Lagrange dual function, efficient solvers are developed that leverage infinite-impulse-response based circulant approximations and/or preconditioned conjugate gradient (PCG). The proposed filter design is compared against current linear solvers and is found to have a lower order computational complexity with less required sequential calculations. Filter performance is assessed via hardware-in-the-loop and open-air experimental measurements. © 1965-2011 IEEE.

关键词： Signal to noise ratio

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Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of Intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

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International Journal of Intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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Unconventional spin-orbit torques from sputtered MoTe2 films

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Physical Review B 2024年第2期110卷 024426-024426页

作者： Shuchen Li Jonathan Gibbons Stasiu Chyczewski Zetai Liu Hsu-Chih Ni Jiangchao Qian Jian-Min Zuo Jun-Fei Zheng Wenjuan Zhu Axel Hoffmann Department of Materials Science and Engineering and Materials Research Laboratory Department of Physics Department of Electrical and Computer Engineering Danbury Connecticut 06810 USA

Materials with strong spin-orbit coupling and low crystalline symmetry are promising for generating large unconventional spin-orbit torques (SOTs), such as in-plane fieldlike (FL) torques and out-of-plane dampinglike (DL) torques, which can effectively manipulate and deterministically switch an out-of-plane magnetization without the need for additional external in-plane magnetic fields. Here, we report SOTs generated by magnetron-sputtered 1T′MoTe2/Permalloy (Py; Ni80Fe20)/MgO heterostructures using both spin-torque ferromagnetic resonance (ST-FMR) and second harmonic Hall measurements. We observed unconventional FL and DL torques in our samples due to spins polarized normal to the interface of MoTe2 and Py layers, and studied the influence of crystallographic order and MoTe2 layer thickness on the SOTs. By comparing the Raman spectra of 1T′MoTe2 samples prepared in different ways, we found a tensile strain in sputtered MoTe2 films, which might further enhance the generation of unconventional torques by reducing the symmetry of 1T′MoTe2.

关键词： Spin injection Spin-orbit torque Spintronics Ferromagnetic resonance Raman spectroscopy

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework 63

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anand, Pallov Aguiar, A. Pedro Sujit, P.B. Universidade Do Porto SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Porto Portugal Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal462060 India

ISBN: (纸本)9798350316339

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic Programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments. © 2024 IEEE.

关键词： Multi agent systems

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Waseem, Aadil Chan, Clarence Wu, Xihang Liu, Yujie Shen, Yifan Kapritsos, Manos Norris, Theodore B. Li, Xiuling Microelectronic Research Center Chandara Family Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78758 United States Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

To enhance solar photo absorption of III-N semiconductors, we demonstrate Arsenic implantation at elevated temperatures, improving doping activation and reducing crystal damage. CLEO 2024 © Optica Publishing Grou... 详细信息

ISBN: (纸本)9781957171395

关键词： Gallium nitride

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Gallium nitride

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年第12期12卷 20707-20721页

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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